基于Hopfield神经网络和禁忌搜索的软/硬件功耗划分

Power-related hardware/software partitioning based on Hopfield neural network and tabu search

在目前全球倡导"低碳经济"的背景下,随着嵌入式系统大量而广泛的使用,嵌入式软件功耗已成为嵌入式系统设计的一个关键因素,而软/硬件划分是嵌入式软件功耗优化的一种重要方法。首先在性能约束条件下,建立以嵌入式软件功耗为目标的软/硬件双路划分模型;然后,提出了一种基于离散Hopfield神经网络(HNN)和禁忌搜索(TS)融合的求解算法,采用离散Hopfield算法作为主算法能较快地获得可行解,使用禁忌搜索算法"禁忌"当前解而转移到目标函数的其他极小点,从而可跳出局部最优解而快速趋于全局最优解;最后,仿真实验表明,与同类算法相比,该算法不但具有搜索速度上的优势,而且求得全局最优解的概率更高。

Nowadays, as low carbon economy has been advocated worldwide, the power consumption of embedded software has become a critical factor in embedded system design. The hardware/software partitioning is an important method of embedded software power optimization. Firstly, this paper constructed a hardware/software bi-partitioning model with the goal of embedded software power consumption under the constraints of performance; then, a hybrid algorithm was proposed based on the fusion of discrete Hopfield Neural Network （HNN） and Tabu Search （ TS）, in which HNN as the main method could quickly obtain a feasible solution of partitioning, and the TS algorithm could ＂taboo＂ the current solution and transferred to the other minimum points that could jump out from the local optimal solution. Lastly, the experimental results show that the proposed algorithm posses better time performance and higher probability of acquiring the global optimal solution in contrast with other similar algorithms.